Abstract

This thesis presents designs for a series of clinical trials where instead of
designing clinical trials individually, each of the trials is designed as part of
a series of trials. The framework of the design is based on a combination of
classical frequentist and Bayesian approaches which is sometimes known as
the hybrid approach. The unknown parameter of the treatment efficacy is
assumed to be random and follows a prior distribution in the design stage
but at the end of the trial a frequentist test statistic is used on the observed
data to infer the parameter. The design introduced in Chapter 5 aims to
determine an optimum sample size for each trial by optimizing the average
power of each trial and the overall resources while fixing the conventional
type I error. The design has the
exibility to either run sequentially or
concurrently. The design is then extended to allow interim analyses in each
trial (Chapter 6). The focus of the extended design is on a series of Bayesian
decision-theoretic phase II trials and one frequentist phase III trial. At each
interim stage, a decision is made based on the expected utilities of subsequent
actions. There are four possible actions to choose from, namely, to continue
the current trial by recruiting more patients, to initiate a new phase II trial,
to abandon the development plan or to proceed to a phase III trial with
this treatment against a control arm. For the last action, the phase III trial
is designed with the hybrid methodology as described above. Finally, the
prior distributions for each treatments are assumed to be correlated and as
information is gathered from the previous and current trials, the current and
following prior distributions are updated (Chapter 7).